Contrast enhancement plays an important role in the improvement of visual quality for computer vision, pattern recognition, and the processing of digital images.
Generally, traditional histogram equalization (THE) can be used to enhance the image contrast by using probability density function (PDF). However, the above method uses the entirety of the information of the histogram, so it is impossible to maintain the brightness of the original image.
To solve the shortcoming of the traditional histogram equalization, variable methods which based on the traditional histogram equalization are proposed to maintain the brightness of the primary image. Most of these methods are accomplished by histogram segmentation. Nevertheless, these methods are prone to produce distortions of the local features.
Typically, a conventional gamma correction method may rapidly enhance image contrast by adjusting the gamma variables in the function. However, gamma correction cannot provide dynamic adjustment of image contrast enhance to every dimmed image. In order to solve the shortcoming, a method called dynamic contrast ratio gamma correction (DCRGC) combines histogram normalization and reverse-gamma correction is proposed to cope with the dynamic contrast enhancement problem. Unfortunately, this method still cannot automatically obtain contrast enhancement from variable controls.